Activity capability monitoring

ABSTRACT

A method of monitoring activity capabilities of a subject, including determining sensor data for each of a plurality of sensors, the sensors being positioned within a living environment of the subject and the sensor data being indicative of activities performed by the subject; for each of a plurality of activity domains, determining a domain score indicative of a level of activity within the respective activity domain, the domain score being determined using sensor data from a respective combination of sensors within the respective domain; determining a reference activity level using reference domain scores during a reference time period; determining a current activity level using current domain scores during a monitoring time period; and generating an activity indicator in accordance with the current activity level and the reference activity level, the activity indicator being indicative of differences between the current activity level and the reference activity level, thereby providing feedback on the activity capabilities of the subject.

BACKGROUND OF THE INVENTION

The present invention relates to a method and apparatus for monitoringactivity capabilities of an individual, for example to determine theability of an individual to perform activities of daily living.

DESCRIPTION OF THE PRIOR ART

The reference in this specification to any prior publication (orinformation derived from it), or to any matter which is known, is not,and should not be taken as an acknowledgment or admission or any form ofsuggestion that the prior publication (or information derived from it)or known matter forms part of the common general knowledge in the fieldof endeavour to which this specification relates.

Activity of Daily Living (ADL) has become the de facto clinical standardto assess the functional status of older people to be able live safelyand independently in their home. Over past forty years, more than fortydifferent ADL indexes have been developed to determine fundamentalfunctional disability status of both patients and population. Measuresof functional ability outlined by the ADL have become routine in theassessment of functional status of older people and believed to be agood predictor of a wide range of health-related behaviour in seniors.

Among them, the Katz ADL scale is a tool for assessing an older adult'sability to bathe, dress, use the toilet, transfer, remain continent, andfeed her/himself. It's also used for evaluating changes in response toillness. The Katz index is easy to use and adaptable to most clinicalassessment. ADL scales have also been widened to assessment thataccommodate more sophisticated functional requirements full range ofactivities necessary for independent living such as ability to cope withfinancial transactions, neurological disorders and cognitive impairmentprevalent with aging.

The current approach to measuring ADL is in a clinical setting prior todischarge from rehabilitation, for example by a clinician such as anoccupational health therapist. In some cases, the discharge may bedelayed (hence, increased length of stay) if modifications of thepatient's home environment may be required to be done to adjust fortheir limited functional ability. Some form of ADL is also used ingeriatric assessment for eligibility to residential care, which is againperformed in a similar manner by a clinician or the like, in a clinicalenvironment. However, this approach suffers from a number of drawbacks.

Firstly, it is not representative of an individual's living environment,and doesn't therefore necessarily take into account the individual'severyday living routines of activities. Secondly, it is subjectivemeasure due to it being based on a clinical staff's observation andcorresponding self-reported responses from the individual in question,and can therefore vary between assessors and individuals, meaning theoutcomes are not objective or consistent. There is also evidence showingvariations in ADL assessment, with results likely to be skewed byself-reported questions being interpreted differently due to individualsfrom various culture, language, and education backgrounds. Similarissues arise due to communication barriers from cognitive impairmenthaving significant implications on achieving reliable ADL assessment.Thirdly, current assessments are clinically resource intensive,particularly from a home setting, making them impractical for long termcare of the elderly or disabled populations. Fourthly, ADL can vary overtime and therefore not practical to provide regular ADL assessment by aclinician.

Attempts have been made to implement sensing systems to reduce workloadand provide more objective assessment. However, the majority of thesesystems still rely on assessment in a clinical setting and are nottherefore reflective of actual typical activities performed by theindividual. Additionally, such systems often rely on wearable sensors,which have the drawback of being intrusive and requiring compliance bythe individual, for example in ensuring they wear the sensor. Othersystems tend to focus on limited aspect of living, and do notobjectively assess a complete range of tasks performed by theindividual.

GB-2,352,815 describes an equipment monitor which is capable of learninghow to respond to particular inputs (e.g. a neural network) is connectedto a plurality of instruments (e.g. medical instruments in an intensivecare ward or instruments in an industrial plant). During an initialtraining session, a human supervisor monitors the instruments to ensurethat no potential alarm condition is encountered whilst the monitorassimilates the gamut of sensor data representative of “safe” or“healthy” conditions. Thereafter the equipment is left to sensor data analarm if the collection of sensor data it is monitoring strays out ofthe range encountered during the training session. A button may beprovided for indicating to the monitor that responses which give rise tofalse alarms should be included in its “safe” responses. The monitor maybe provided with some rules prior to its learning phase.

However, this is focussed on fall detection and is not applicable morebroadly to other aspects of activity capability assessment, or assessinga gradual decline in capabilities.

US2014/0281650 describes a passive monitoring system for use in aresidential independent living framework. In some exemplary embodiments,the system may be used to alert a primary caregiver of a possibledecline or change in an activity of daily living (“ADL”) of themonitored individual. The system may collect usage data associated with,for example, electrical devices at the living quarters of the monitoredindividual. The collected data may include data pairs of time samplesand voltage data associated with one or more electrical devices that themonitored individual is expected to utilize. Applying various filters tothe collected data, a possible decline or change in ADL of the monitoredindividual may be identified.

However, this system focusses only on electrical device usage and is notapplicable more broadly to activities that do not use electricaldevices, as is the case with many activities of daily living.

U.S. Pat. No. 8,810,388 relates to a system and method for monitoringthe location, movement and health of one or more individuals within anenvironment by a monitoring individual, such as a care giver. The systemused includes optional monitoring devices including a wirelesstransceiver, access point devices including a wireless transceiver, ahub access point device including a wireless transceiver, and a localcomputing device. The system is programmed such that it has thecapability to operate with or without the measure of time of flightvalue from the optional monitoring devices such that the system has thecapability of monitoring the location, movement and health of anindividual whether or not the individual is wearing the monitoringdevice.

However, this system is limited to movement monitoring and provides noguidance regarding capability to perform activities more broadly.

U.S. Pat. No. 8,184,001 describes a wireless contextual prompting devicethat provides contextual (context-aware) prompting in the home forapplications such as Activities of Daily Living (ADL) monitoring,medication adherence, journaling, social messaging and coaching. Thedevice combines the advantages of a small, wireless, battery-operatedsensor that may be easily mounted at critical places in a person's dailyroutine with a low-power, high-contrast display panel that may be palmsized. The context may be displayed on the display screen as images,icons and/or text such that it is easy to interpret warnings by theyoung, elderly, or the language-challenged.

However, this arrangement focusses on providing prompting toindividual's to assist them with performing tasks, which in many casesis intrusive, and does not objectively assess the individual's abilityto perform the task.

U.S. Pat. No. 8,798,573 describes a monitoring system capable ofmonitoring the Activities of Daily Living (ADL) of one or more personsoccupying a building. The monitoring system includes an informationhandling system having a radio-frequency (RF) scanner capable ofscanning the RF ambient environment of the building. When an individualuses devices in the building that emit RF sensor data or emissions, theRF sensor data are detected by the RF scanner and analyzed by theinformation handling system. The characteristics of the detected RFsensor data are compared to a database of signature of known devices. Ifa detected RF sensor data matches the signature of a known device, theuse of the device is logged into a database for ADL analysis.

This suffers from the drawback of being solely focused on RF sensor datadetection and provides limited ability to detect a wide range ofactivities.

US2005/0234310 describes a method and related system to, among otherthings, automatically infer answers to all of the ADL questions and thefirst four questions of the IADL in the home. The inference methodsdetect the relevant activities unobtrusively, continuously, accurately,objectively, quantifiably and without relying on the patient's ownmemory (which may be fading due to aging or an existing healthcondition, such as Traumatic Brain Injury (TBI)) or on a caregiver'ssubjective report. The methods rely on the judicious placement of anumber of sensors in the subject's place of residence, including motiondetection sensors in every room, the decomposition of each relevantactivity into the sub-tasks involved, identification of additionalsensors required to detect the relevant sub-tasks and spatial-temporalconditions between the sensor data of sensors to formulate the rulesthat will detect the occurrence of the specific activities of interest.The sensory data logged on a computing device (computer, data loggeretc.), date and time stamped, is analyzed using specialist data analysissoftware tools that check for the applicable task/activity detectionrules. The methods are particularly useful for the continued in-homeassessment of subjects living alone to evaluate their progress inresponse to medical intervention drug or physical therapy or decline inabilities that may be the indicator of the onset of disease over time.Measuring the frequency of each activity, the time required toaccomplish an activity or a subtask and the number ofactivities/subtasks performed continuously over time can add extremelyvaluable quantification extensions to the existing ADL and IADLevaluation instruments, as it will not only reveal important informationsetting up a baseline for activity levels for each activity, but willalso easily allow the detection of any drift from these personalizednorms.

However, this requires a rules based approach, which is complex andprone to error in the event that rules are not suitable for a givencircumstance. Furthermore, the document only describes the use of yes/noanswers in assessing specific limited activities, which provides limitedfeedback in terms of quantifying abilities of an individual, and healthstatus more generally.

SUMMARY OF THE PRESENT INVENTION

In one broad form the present invention seeks to provide a method ofmonitoring activity capabilities of a subject, the method including, inat least one processing device:

-   -   a) determining sensor data indicative of sensor readings for        each of a plurality of sensors, the sensors being positioned        within a living environment of the subject and the sensor data        for each sensor being at least partially indicative of one or        more activities performed by the subject;    -   b) for each of a plurality of activity domains, determining a        domain score indicative of a level of activity within the        respective activity domain, the domain score being determined        using sensor data from a respective combination of sensors        associated with the respective domain;    -   c) determining a reference activity level using reference domain        scores measured during a reference time period;    -   d) determining a current activity level using current domain        scores measured during a monitoring time period; and,    -   e) generating an activity indicator at least partially in        accordance with the current activity level and the reference        activity level, the activity indicator being at least partially        indicative of differences between the current activity level and        the reference activity level, thereby providing feedback on the        activity capabilities of the subject.

In one broad form the present invention seeks to provide apparatus formonitoring activity capabilities of a subject, the apparatus including:

-   -   a) a plurality of sensors, the sensors being positioned within a        living environment of the subject; and,    -   b) at least one processing device that:        -   i) determines sensor data indicative of sensor readings for            each of a plurality of sensors, the sensors being positioned            within a living environment of the individual and the sensor            data for each sensor being at least partially indicative of            one or more activities performed by the individual;        -   ii) for each of a plurality of activity domains, determines            a domain score indicative of a level of activity within the            respective activity domain, the domain score being            determined using sensor data from a respective combination            of sensors associated with the respective domain;        -   iii) determines a reference activity level using reference            domain scores measured during a reference time period;        -   iv) determines a current activity level using current domain            scores measured during a monitoring time period; and,        -   v) generates an activity indicator at least partially in            accordance with the current activity level and the reference            activity level, the activity indicator being at least            partially indicative of differences between the current            activity level and the reference activity level, thereby            providing feedback on the activity capabilities of the            subject.

Typically the method includes:

-   -   a) determining an activity level score by combining the domain        scores;    -   b) determining the reference activity level using the activity        level score measured during the reference time period; and,    -   c) determining the current activity level using the activity        level score measured during the monitoring time period.

Typically the combination includes at least one of:

-   -   a) a sum; and,    -   b) a weighted sum.

Typically the method includes:

-   -   a) determining an activity pattern indicative of relative values        of domain scores;    -   b) determining the reference activity level using the activity        pattern measured during the reference time period; and,    -   c) determining a current activity level using a current activity        pattern measured during the monitoring time period.

Typically the method includes:

-   -   a) comparing the current activity level to the reference        activity level; and,    -   b) generating the activity indicator at least partially in        accordance with results of the comparison.

Typically the method includes comparing at least one of:

-   -   a) each current domain score to an equivalent reference domain        score;    -   b) each current domain score to a respective reference range        derived from an equivalent reference domain score;    -   c) a current activity level score to a reference activity level        score;    -   d) a current activity level score to a respective reference        range derived from a reference activity level score;    -   e) a current activity pattern to a reference activity pattern;        and,    -   f) a current activity level to a reference activity level        measured during a corresponding time period.

Typically the method includes:

-   -   a) determining a condition suffered by the subject; and,    -   b) determining, at least partially in accordance with the        condition, at least one of:        -   i) an activity level score;        -   ii) a domain score;        -   iii) a reference domain score range; and,        -   iv) a reference activity level range.

Typically the method includes:

-   -   a) determining an action rule; and,    -   b) selectively performing an action in accordance with the        action rule and in response to the results of the comparison.

Typically the action includes:

-   -   a) generating an alert notification; and,    -   b) providing the alert notification to a user by transferring        the alert notification to a client device of the user via a        communications network.

Typically the method includes generating a representation indicative ofat least one of:

-   -   a) results of a comparison;    -   b) current domain scores;    -   c) reference domain scores;    -   d) current activity level scores;    -   e) reference activity level scores;    -   f) current activity level patterns;    -   g) reference activity level patterns; and,    -   h) the activity indicator.

Typically the method includes providing the representation to a clientdevice via a communications network.

Typically the method includes determining a domain score using sensordata from a respective combination of sensors.

Typically the respective combination of sensors for each domain isdetermined based on at least one:

-   -   a) a sensor type; and,    -   b) a sensor location.

Typically the sensors include at least one of:

-   -   a) motion sensors;    -   b) power sensors that monitor operation of appliances;    -   c) temperature sensors;    -   d) humidity sensors;    -   e) accelerometers; and,    -   f) door sensors.

Typically the activity domain includes:

-   -   a) hygiene;    -   b) nutrition;    -   c) mobility;    -   d) transfer; and,    -   e) dressing/grooming.

Typically the method includes, for at least one domain:

-   -   a) identifying events using sensor data from the sensors; and,    -   b) determining the domain score using at least one of:        -   i) a sum of a number of events during a time period; and,        -   ii) a sum of a number of clusters of events during a time            period.

Typically the method includes identifying events by comparing sensordata to a number of signatures, each signature being indicative of arespective event.

Typically the apparatus includes a hub provided in the livingenvironment, the hub being adapted to communicate with each of thesensors and provided the sensor data to the at least one processingdevice, via a communications network.

Typically the apparatus includes a processing system including at leastone processing device, the processing system communicating with one ormore client devices via communications network, to at least one of:

-   -   a) provide alert notifications to the client devices; and,    -   b) allow the client devices to display a representation        indicative of at least one of:        -   i) results of a comparison;        -   ii) current domain scores;        -   iii) reference domain scores;        -   iv) current activity level scores;        -   v) reference activity level scores;        -   vi) current activity level patterns;        -   vii) reference activity level patterns; and,        -   viii) the activity indicator.

It will be appreciated that the broad forms of the invention and theirrespective features can be used in conjunction and/or independently, andreference to separate broad forms of the invention is not intended to belimiting.

BRIEF DESCRIPTION OF THE DRAWINGS

An example of the present invention will now be described with referenceto the accompanying drawings, in which:

FIG. 1 is a flow chart of an example of a process for monitoringactivity capabilities of an individual;

FIG. 2 is a schematic diagram of a second example of apparatus formonitoring activity capabilities of an individual;

FIG. 3 is a schematic diagram of an example of a base station processingsystem;

FIG. 4 is a schematic diagram of an example of a client device;

FIG. 5 is a flow chart of an example of a method for monitoring activitycapabilities of an individual;

FIGS. 6A and 6B are a flow chart of a method of monitoring activitycapabilities of an individual;

FIG. 7 is a schematic diagram showing a typical sensor arrangement formonitoring activity capabilities of an individual;

FIG. 8 is a flow chart of an example of a technique for calculatingdomain scores;

FIGS. 9A to 9F are graphs showing examples of different domain scoresover time;

FIG. 10A is a schematic diagram of an example of motion sensor firings;

FIG. 10B is a schematic diagram of an example of humidity value changesin a bathroom;

FIG. 10C is an example of accelerometer readings from an individual'sbed;

FIG. 10D is an example of accelerometer angle changes indicative of bedmovements;

FIG. 10E is a schematic diagram indicative of sensor firing associatedwith meal preparation;

FIG. 11A is a schematic diagram of wellbeing scores;

FIG. 11B is a graph illustrating domain scores for an individual; and,

FIGS. 12A to 12C are schematic diagrams of example of user interfaces.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An example of a method for monitoring activity capabilities of a subjectwill now be described with reference to FIG. 1.

For the purpose of illustration, it is assumed that the process isperformed at least in part using one or more electronic processingdevices forming part of one or more processing systems, such as computersystems, servers, or the like, and which are optionally connected to oneor more client devices, such as mobile phones, portable computers or thelike, via a network architecture, as will be described in more detailbelow.

The processing device is in communication with a plurality of sensors,the sensors being positioned within a living environment of the subject,such as the subject's home. The sensors can be of any suitable form andare typically adapted to sense respective characteristics of thesubject's activities, with a range of different sensors being providedallowing different characteristics of activities to be monitored. Thiscan include, for example motion sensors that detect movement of thesubject, power sensors that monitor operation of appliances, temperaturesensors for detecting localised temperature changes, humidity sensorsfor detecting humidity in the environment, accelerometers for detectingmovement of furniture or other items, contact sensors that detect dooropening, or the like.

The sensors are typically mounted in the subject's living environment,so that an assessment can be made of the subject's activitycapabilities, whilst the subject is performing day-to-day activities. Byhaving the sensors mounted in the environment as opposed to, forexample, being worn, the system is as unobtrusive as possible and avoidsthe need for any specific interaction by the subject. This isparticularly important in the context of elderly or other impairedsubjects that might struggle in order to effectively use worn sensors.

However, it will also be appreciated that data collected by othersensors, such as physiological sensors could also be used in addition tothe use of sensors fixed within the environment. This can be used tosupplement the information collected by the fixed sensors, for exampleallowing information regarding physiological parameters of the subjectto be collected, although this is not essential and the system isadapted to be able to operate without such sensors, for example in thecase that the subject forgets to wear the sensors.

In this example, at step 200 the processing device 110 determines sensordata indicative of sensor readings for each of the plurality of sensors,with the sensor data being at least partially indicative of activitiesperformed by the subject. Thus, for example, motion sensors will be usedto detect movement of the subject within their living environment whilstsensors such as accelerometers can be used to detect transitions fromseated to standing positions, movement in bed or the like. Temperaturesensors can be used to detect activities such as bathing or cookingwhilst monitoring of appliances can be performed, for example, to detectfood preparation activities or the like.

The sensor data can be received directly from the sensors. However,alternatively, the processing device could determine the sensor data byretrieving previously collected sensor data from a store, or receivingthe sensor data from an intervening device, such as a hub, as will bedescribed in more detail below.

At step 210, the processing device determines a domain score for each ofa plurality of different activity domains. The domain score isindicative of a level of activity within the respective domain and is atleast in part determined using sensor data from a respective combinationof sensors.

Thus, different domains are defined corresponding to different types ofactivity. In general, these will include, but are not necessarilylimited to domains including: meals, mobility, transfer, hygiene anddressing. These domains correspond to typical activities that a subjectwould be expected to perform as part of their day-to-day living. Thus,monitoring scores across each of these five domains allows an assessmentto be made as to whether the subject is capable of caring forthemselves, or whether additional assistance or interventions areneeded.

It will be appreciated that the particular combination of sensor dataused in order to determine the score will vary depending on the natureof the domain. For example, the meal domain will typically requiremonitoring of sensors that can detect meal preparation activities suchas cooking, use of appliances, such as a kettle, oven or stove, oraccessing food storage areas, such as cupboards, fridges, freezers, orthe like. This is also typically constrained to a particular area, suchas the kitchen.

In the case of hygiene, sensing is typically performed primarily in abathroom or toilet, and could examine sensors relating to activation ofsanitary fittings, as well as temperature and/or humidity settings,which can in turn be indicative of showering or the like. In the case ofmobility sensing, this will typically be performed largely on the basisof movement sensors distributed throughout the living environment.Transfer, which examines the ability of a subject to change posture, canbe performed on the basis of sensor data from accelerometers mountedwithin furniture, such as a bed or seat, as well as motion sensors,allowing an assessment to be made of the capability of a subject tostand, sit down, or otherwise change posture. Finally, in the case ofdressing or grooming, this could include examining access to clothingstorage, such as wardrobes, as well as use of related appliances, suchas washing machines, irons, or the like.

It will be appreciated from this that the manner in which each domainscore is calculated will vary depending on the nature of the domain andthe type of sensors available, and specific examples will be describedin more detail below.

At step 220, a reference activity level is established using referencedomain scores measured during a reference time period. The referenceactivity level is used as a baseline and is measured in situ in order toestablish a typical level of activity for the user, in their homeenvironment. This is typically performed over an extended period oftime, such as between one week and one month, as this allows anunderstanding of the typical degree of variation in the level ofactivity for the subject, and also enables the system to take intoaccount differences in daily schedules of the subject, for example asthe subject may have a set routine in which certain activities, such aswashing or cleaning, or times absent from the home, are performed in aregular pattern. However, this is not essential, and in other examples,the reference time period could be based on shorter time periods, andcould be based on a rolling time period, so that the reference timeperiod is always taken to be the activity levels measured on a previousday.

During this reference time period, traditional monitoring of the usercan also be performed in order to assess whether the reference activitylevel corresponds to an acceptable capability level, or whether this isdeficient for some or all of the domains. This can be used to act as areference point so that the determined domain scores can be correlatedto an understood clinical assessment of activity capabilities. Thus, forexample, a low mobility score for a user can be indicative of aninability to be mobile, or may result from the person being sedentary bynature. Accordingly, the assessment is made to establish a baseline thatis typical for the subject, assuming a certain ability to performactivities and hence a certain level of ability to care for themselves.

At step 230 a current activity level is determined using current domainscores measured during a monitoring time period. Thus, after thebaseline has been established, a similar process is performed in orderto measure the current activity levels of the subject. This is performedrepeatedly after the reference activity levels have been establishedallowing changes from the reference activity levels to be monitored.Monitoring of current activity levels can be performed on a regularbasis, such as daily. In one example, the activity levels could becompared to comparable activity levels measured during the referencetime period, based on a subject's schedule, so that for example, anactivity level measured on a set day (such as a Wednesday), is comparedto reference activity levels established for the same day (i.e.Wednesdays) during the reference time period. Alternatively, as opposedto performing day-to-day comparisons, comparisons could be performedbetween respective types of day, such as to compare activity levels forweekdays with reference activity levels established for weekdays, andsimilarly comparing activity levels for weekends to reference activitylevels for weekends.

At step 240, an activity indicator is generated at least partially inaccordance with the current activity level and the reference activitylevel. The activity indicator is generated in such a way that it is atleast partially indicative of differences between the current activitylevel and the reference activity level. Thus, this could simply be avisual display of the reference and current activity levels, allowingusers to perform a visual side-by-side comparison of the reference andcurrent activity levels. Alternatively however, some form of analysiscould be performed, for example to compare the reference and currentactivity levels, and display results of the comparison. Further exampleswill be described in more detail below.

In any event this allows the activity level indicator to providefeedback on the activity capabilities of the subject, which in turnallows an assessment of a subject's activity capabilities to beperformed. For example, this allows third party users, such as carers,medical practitioners, family, or the like, to readily identify changesin activity capability and, in particular, degradation in activitycapability, which can in turn be used to identify when intervention orassistance may be required.

In one example, this can be performed remotely, avoiding the need forongoing onsite monitoring, thereby significantly reducing the burden onhealthcare workers, whilst also allowing changes in capabilities to beidentified without requiring input or feedback from the user, who maynot be cognisant of a change, or unable or unwilling to communicate achange.

In any event, the above described system approach allows the activitycapabilities of a subject to be monitored within the subject's ownliving environment, and with regard to the subject's day-to-dayactivities, as established during the reference time period. Thisensures the assessment of activity capabilities are made in regard toactivities that are actually of relevance to the subject in theircurrent living circumstances. By way of example, if a subject lives in asingle floor dwelling, the fact that the subject is unable to climbstairs may not be relevant to their ability to continue living at home,and hence this avoids the need to assess this capability.

Furthermore, monitoring is performed using sensors mounted within theliving environment. This ensures that the activity capabilities of thesubject can be quantified based on an objective assessment of thesubject's capabilities performed in an automated manner, rather thanrelying on a subjective assessment of the subject's capabilities.Furthermore, this is achieved without the need for wearable sensors,which can be intrusive and may inadvertently be misused. This alsoallows a wider variety of sensors to be employed, allowing for greaterscope in capability assessment.

A number of further features will now be described.

In one example, the method includes determining an activity level scoreby combining the domain scores. The reference activity level can then bedetermined using the activity level score measured during the referencetime period, whilst the current activity level is determined using theactivity level score measured during the monitoring time period. Thisallows a single numerical overall score to be calculated, providing astraightforward and easy to understand indicator of capability.

The activity level score can be determined using a simple sum of thedomain scores, for example as a simple sum of the domain scores assessedfrom equally weighted domains, but alternatively could be calculatedbased on another mathematical function, such as a weighted sum, forexample based on a degree of priority, relevance or severity of acondition, allowing this to take into account that different domains mayhave a different impact on a subject's ability to manage withoutassistance or intervention. This can be used to prioritise particulardomains over others when an overall score is calculated, for example totake into account domains of particular relevance to the subject. Thus,for a subject with dietary complications, a meal activity domain mightbe given a higher weighting and hence higher priority than otherdomains, such as transfer or movement. It will be appreciated from thisthat the weighting used may depend on circumstances relevant to thesubject, such as one or more conditions from which the subject suffers.

Whilst a combination may be used to provide an overall score, however,this is not essential and other assessments could be performed. Forexample, the method could include determining an activity patternindicative of relative values of domain scores, determining thereference activity level using the activity pattern measured during thereference time period and then determining a current activity levelusing a current activity pattern measured during the monitoring timeperiod. Thus, this will examine if there is a change in the relativeactivity in different domains, highlighting for example, that whilst asubject is still mobile, they may have lost appetite. The activitypattern may also be used as a signature to identify certain events. Forexample, if a subject's mobility suddenly drops dramatically, this couldbe indicative of a fall or other similar event.

As a further alternative, assessment could be performed based onexamination of the domain scores for each of the domains respectively,performing a comparison of each current domain score to a correspondingreference domain score.

In one example, the method includes comparing a current activity levelto the reference activity level and then generating the activityindicator at least partially in accordance with results of thecomparison. Thus, this allows changes from the reference activity levelto be easily identified. The comparison can be performed in any one of anumber of ways which could include any one or more of comparing eachcurrent domain score to an equivalent reference domain score, comparingeach current domain score to a respective reference range derived froman equivalent reference domain score, comparing a current activity levelscore to a reference activity level score, comparing a current activitylevel score to a respective reference range derived from a referenceactivity level score and a current activity pattern to a referenceactivity pattern.

Additionally and/or alternatively, a current activity level can becompared to a reference activity level measured during a correspondingtime period, such as the same day from an earlier week. Thus, forexample, if the reference activity levels show patterns or trends, suchas particular levels of activity on particular days of the week, as mayoccur for a subject's day to day routine, then the current activitylevel measured for a particular day could be compared to referenceactivity levels measured on the same day during the reference timeperiod, thereby allowing the subject's particular routine to be takeninto account.

Thus, it will be appreciated from this that comparisons could beperformed between overall activity scores and/or subject domain scores.Additionally, whilst direct comparison could be performed, alternativelycomparison could be to ranges established using the reference activitylevel or reference domain scores. Thus as opposed to comparing measureddomain scores to absolute domain score values, the comparison could beto a value range based on the measured reference domain scores, forexample based on a standard deviation of reference domain scores over adefined time period. The reference ranges could be used to reflect dayto day differences in activity levels, as well as typical variationsthat would be expected for the subject, optionally taking into accountconditions suffered by the subject. Thus the range could be determinedfrom the reference activity level scores or domain scores, for examplelooking at a variation of activity level or domain scores for aparticular domain, over the reference time period. Thus, this canprovide a longitudinal analysis in which data gathered over a timeperiod is used to predict an individual's own profile. Alternatively,this might be established based on a sample population, of typicallysimilar individuals, and could also be specific to a particularcondition suffered by the subject. For example, this can be used todefine a restricted range in the event that activities in a particulardomain are critical, such as ensuring adherence to diet for a diabetic,or more lax in the event that the domain is of less importance to thesubject's wellbeing. Accordingly, in one example, the method includesdetermining a condition suffered by the subject and determining acombination of domains, domain scores, or domain score ranges, at leastpartially in accordance with the condition, thereby allowing domains ofmost relevance to the condition to be given priority in the assessment.

Having performed the comparison, the method can further includedetermining an action rule and selectively performing an action inaccordance with the action rule and in response to the results of thecomparison. In this regard, action rules can be determine for eachsubject being monitored, with the action rule defining one or moreactions that might need to be performed depending on the results of thecomparison.

Whilst any form of action could be performed, in one example, the methodincludes selectively generating an alert notification in response toresults of the comparison and providing the alert notification to auser, such as a carer, medical practitioner, family member, or the like.The alert notification can be used to alert the user that an event hasoccurred, such as the subject has failed to eat or move in a definedtime period, allowing appropriate action to be taken, such as allowingthe user to follow up and be provided with appropriate care orintervention.

As part of this process, the method can include identifying at least oneuser depending on the results of the comparison. This allows differentusers to be notified depending on the results of the comparison. Forexample, if the comparison indicates that mobility has reduced but notstopped completely, a carer could be alerted. However, if mobility hasceased completely, then emergency services could be notified, allowingappropriate emergency intervention to be provided.

Once the relevant users have been identified, the method typicallyincludes providing the alert notification to at least one user bytransferring the alert notification to a client device of the user via acommunications network. This allows the user to be alertedautomatically, using a device such as a smart phone, mobile phone or thelike, allowing them to take appropriate action in a rapid manner. Thealert could simply be a notification that an issue has been detected,but could also provide additional information, such as a representationindicative of the activity indicator, allowing the relevant user to makean assessment of what action, if any, should be taken.

A representation of the activity indicator and/or other informationcould also be generated and provided to users for viewing at othertimes, such as on demand. This could be achieved in any appropriatemanner, such as by providing the representation to a client device via acommunications network in response to a request. This could be providedin any suitable manner, such as through a user interface displayed by anapplication, as part of a webpage, or the like. The representation couldinclude a range of different information, including but not limited toresults of a comparison, current domain scores, reference domain scores,current activity level scores, reference activity level scores, currentactivity level patterns, reference activity level patterns and theactivity indicator.

As previously mentioned, the domain score can be determined using sensordata from a respective combination of sensors. This allows differentcombinations of sensors to be used in establishing different domainscores, as will be described in more detail below.

The sensors are typically mounted in an environment used by the subject,and in particular are typically provided throughout a living environmentof the subject, allowing monitoring to be performed for all of thesubject's day-to-day activities. Determining a domain score cantherefore be performed based at least in part on a location of thesensors. For example, this can be performed by monitoring sensorsprovided within a respective room, so hygiene may be largely based onsensors located within a bathroom or toilet facility, whereas mealdomain scores will typically be based on sensors provided in a kitchenand/or dining room.

Additionally, the sensors used will also typically depend on a sensortype. For example, motion sensors could be used to determine mobility,whilst operation of appliances or the like, can be used to assessactivities such as meal preparation. Typically the sensors include, butare not limited to motion sensors, power sensors that monitor operationof kitchen appliances, temperature sensors, humidity sensors,accelerometers and door sensors, such as reed switches mounted to thedoor.

In one example, the process of monitoring the sensors includesidentifying events using sensor data from the sensors and determiningthe domain score using either a sum of a number of events during a timeperiod or a sum of a number of clusters of events during a time period.The events could be identified in any suitable manner, and this couldinclude comparing sensor data to a number of signatures, each signaturebeing indicative of a respective event. Thus the signature could beindicative of a particular sensor reading or pattern of sensor readings,and could include readings from multiple sensors simultaneously,allowing different events to be more accurately identified. Thus, thisallows individual events to be easily identified, with the events beingviewed collectively in order to allow a score for each domain to beestablished.

An example of a system for use in activity monitoring will now bedescribed with reference to FIG. 2.

In this example, the system 200 includes a number of sensors 210 each ofwhich is coupled to a hub 220, which in turn is in communication withone or more client devices 230 and/or a processing system, such as aserver 250, via one or more communications networks 240.

In this example, the sensors 210 and hub 220 are provided in a subject'sdwelling D, which could be a house, apartment, or the like. The sensors210 are typically mounted throughout the dwelling, at appropriatelocations, and may be coupled to furniture, fixtures or fittings asrequired. The sensors 210 are therefore typically wirelessly coupled tothe hub 220 using a suitable communications technique, such asBluetooth, WiFi, or the like, allowing for ease of installation,although this is not essential and wired communication could be used.

In one example, the hub 220 includes a hub processor 221, a hub memory222, an optional hub input/output device 223, such as a keyboard anddisplay or touchscreen, an external interface 224 and a sensor interface225, interconnected via a bus 226. The sensor interface 225 is adaptedto wirelessly communicate with the sensors 210, whilst the client deviceinterface 224 is adapted to allow communications with a client device230 either directly, or via an intermediate communications network 240as shown. Although a single external interface is shown, this is for thepurpose of example only, and in practice multiple interfaces usingvarious methods (e.g. Ethernet, serial, USB, wireless or the like) maybe provided. Similarly, the external interface 224 and sensor interface225 could in fact be provided by a single interface, such as a wirelessnetwork interface, and reference to separate interfaces is not intendedto be limiting but rather is for the purpose of illustration.

In use, the hub processor 221 executes instructions in the form ofapplications software stored in the memory 222 to enable communicationwith the sensors, allowing sensor data to be received and provided tothe server 240. As part of this, some processing may be performed, forexample to sample and/or interpret signals from the sensors and generatethe sensor data. The applications software may include one or moresoftware modules, and may be executed in a suitable executionenvironment, such as an operating system environment, or the like.

Accordingly, it will be appreciated that the hub 220 may be formed fromany suitable processing system, such as a suitably programmed computersystem, although this is not essential and the processing system couldbe any electronic processing device such as a microprocessor, microchipprocessor, logic gate configuration, firmware optionally associated withimplementing logic such as an FPGA (Field Programmable Gate Array), orany other electronic device, system or arrangement. The hub could be inthe form of a suitably programmed computer system, server, or otherprocessing system, or could include a communications device, such as amobile phone, smart phone, or the like.

In use, actions performed by the hub 220 are performed by the hubprocessor 221 in accordance with instructions stored as applicationssoftware in the memory 222 and/or input commands received from a uservia the I/O device 223, or commands received from the client device 230or server 250, as will be described in more detail below.

The communications network 240 can be of any appropriate form, such asthe Internet and/or a number of local area networks (LANs) and providesonward connectivity to one or more client devices 230 and the server250, which is in turn coupled to a database 251. It will be appreciatedthat this configuration is for the purpose of example only, and inpractice the hub 220, client devices 230 and servers 250 can communicatevia any appropriate mechanism, such as via wired or wirelessconnections, including, but not limited to mobile networks, privatenetworks, such as an 802.11 networks, the Internet, LANs, WANs, or thelike, as well as via direct or point-to-point connections, such asBluetooth, or the like.

In one example, the server 250 is adapted to interpret sensor data andprovide access to the resulting activity indicators and otherinformation, as well as generating representations and/or alerts asrequired, with these being provided to the client devices 230 asrequired. Whilst the server 250 is a shown as a single entity, it willbe appreciated that the server 250 can be distributed over a number ofgeographically separate locations, for example by using processingsystems and/or databases 251 that are provided as part of a cloud basedenvironment. However, the above described arrangement is not essentialand other suitable configurations could be used.

An example of a suitable server 250 is shown in FIG. 3. In this example,the server includes at least one microprocessor 300, a memory 301, anoptional input/output device 302, such as a keyboard and/or display, andan external interface 303, interconnected via a bus 304 as shown. Inthis example the external interface 303 can be utilised for connectingthe server 250 to peripheral devices, such as the communicationsnetworks 240, databases 211, other storage devices, or the like.Although a single external interface 303 is shown, this is for thepurpose of example only, and in practice multiple interfaces usingvarious methods (e.g. Ethernet, serial, USB, wireless or the like) maybe provided.

In use, the microprocessor 300 executes instructions in the form ofapplications software stored in the memory 301 to allow the requiredprocesses to be performed, including communicating with the clientdevices 230, generating webpages for example including representationsof the activity indicator and/or other information. The applicationssoftware may include one or more software modules, and may be executedin a suitable execution environment, such as an operating systemenvironment, or the like.

Accordingly, it will be appreciated that the server 250 may be formedfrom any suitable processing system, such as a suitably programmedclient device, PC, web server, network server, or the like. In oneparticular example, the server 250 is a standard processing system suchas an Intel Architecture based processing system, which executessoftware applications stored on non-volatile (e.g., hard disk) storage,although this is not essential. However, it will also be understood thatthe processing system could be any electronic processing device such asa microprocessor, microchip processor, logic gate configuration,firmware optionally associated with implementing logic such as an FPGA(Field Programmable Gate Array), or any other electronic device, systemor arrangement.

As shown in FIG. 4, in one example, the client device 230 includes atleast one microprocessor 400, a memory 401, an input/output device 402,such as a keyboard and/or display, and an external interface 403,interconnected via a bus 404 as shown. In this example the externalinterface 403 can be utilised for connecting the client device 230 toperipheral devices, such as the communications networks 240, databases,other storage devices, or the like. Although a single external interface403 is shown, this is for the purpose of example only, and in practicemultiple interfaces using various methods (e.g. Ethernet, serial, USB,wireless or the like) may be provided.

In use, the microprocessor 400 executes instructions in the form ofapplications software stored in the memory 401 to allow communicationwith the server 250, for example to allow for representations of theactivity indicator to be viewed, and to receive alerts, or the like.

Accordingly, it will be appreciated that the client devices 230 may beformed from any suitable processing system, such as a suitablyprogrammed PC, Internet terminal, lap-top, or hand-held PC, and in onepreferred example is either a tablet, or smart phone, or the like. Thus,in one example, the client device 230 is a standard processing systemsuch as an Intel Architecture based processing system, which executessoftware applications stored on non-volatile (e.g., hard disk) storage,although this is not essential. However, it will also be understood thatthe client devices 230 can be any electronic processing device such as amicroprocessor, microchip processor, logic gate configuration, firmwareoptionally associated with implementing logic such as an FPGA (FieldProgrammable Gate Array), or any other electronic device, system orarrangement.

Examples of the operation of the system for monitoring activitycapabilities of a subject will now be described in further detail. Forthe purpose of these examples it will also be assumed that usersinteract with the system via a GUI (Graphical User Interface), or thelike presented on the client device 230, which may be generated by alocal application, or hosted by the server 250 and displayed via asuitable application, such as a browser or the like, executed by theclient device 230. Actions performed by the client device 230 aretypically performed by the processor 400 in accordance with instructionsstored as applications software in the memory 401 and/or input commandsreceived from a user via the I/O device 402. Actions performed by thehub 220 are performed by the processor 221 in accordance withinstructions stored as applications software in the memory 222 and/orinput commands received from a user via the 110 device 223, or commandsreceived from the client device 230 or server 250. Similarly, actionsperformed by the server 250 are performed by the processor 300 inaccordance with instructions stored as applications software in thememory 301 and/or input commands received from a user via the 110 device302, or commands received from the client device 230.

However, it will be appreciated that the above described configurationassumed for the purpose of the following examples is not essential, andnumerous other configurations may be used. It will also be appreciatedthat the partitioning of functionality between the hub 220, clientdevices 230, and servers 250 may vary, depending on the particularimplementation.

For example, in the current configuration, the purpose of the hub 220 isto allow multiple subject sensors to be monitored, with sensor databeing passed onto the server 250 for subsequent analysis. It will beappreciated from this that the use of the hub, whilst convenient is notessential, and similar functionality could be achieved by having thesensors 210 communicate directly with the server 250, for example via anInternet of Things (IoT) type configuration, or by having functionalityperformed by the server 250 implemented locally by the hub or anothersuitable processing system. However, the use of hub 220 and server 250is particularly advantageous as it offloads at least some coordinationof the sensor data to a local device, whilst allowing monitoring to beperformed centrally. This allows for greater oversight by users and alsoallows the analysis performed to be modified dynamically as required.Furthermore, it will be appreciated that whilst two sensors are shown,this is for the purpose of illustration only, and in practice a largernumber of sensors 210 would typically be provided.

An example of operation of the system described above will now bedescribed with reference to FIGS. 5 and 6, which show the process ofdetermining an activity level indicator, and determining monitoringactivity capabilities respectively.

In the example of FIG. 5, at step 500 an indication of sensor readingsis received by the server 250 from the hub 220. This could be performedcontinuously, although more typically the hub 220 will monitor signalsfrom the sensors and then cache these locally, generating andtransferring sensor data to the server 250 on a periodic basis, such asevery hour, twice a day, or the like, depending on monitoringrequirements.

At step 510, sensor data is analysed, with this being used to identifyindividual activity events at step 520. The manner in which this isperformed will vary depending on the nature of the sensor and relevantevents. For example, this could include detecting opening of a door, useof an appliance, movement between rooms or the like, and example of thiswill be described in more detail below.

At step 520, a next domain is selected with this being used to determinea corresponding domain rule at step 530. The domain rule specifies how adomain score is calculated from the sensor data, taking into account theavailable sensors and their respective locations.

In this regard, it will be appreciated that each subject's residence istypically unique, and will include a respective layout of rooms andfurniture. For this reason alone, the particular sensor configurationused in each dwelling will also be unique, depending for example onfactors such as locations in which sensors can be mounted. Additionallyhowever, different sensor configurations could be defined for differentsubjects, depending on their particular functional capabilities, so forexample, for a subject with known mobility restrictions, it may bedesirable to include additional movement sensors, allowing mobility tobe tracked with a higher degree of accuracy. Accordingly, when thedwelling is initially configured with sensors, it is typically to definea custom domain rule for each domain, the domain rule defining theparticular combination of sensors that should be used in order togenerate a respective score.

Additionally, the manner in which the score are generated will varydepending on the nature of the sensors and the particular contributionto activity events relating to the domain. Typically this involvesanalysing the sensor data for each type of sensor, and using this toidentify events, as described at steps 510 and 520 above. Thus, thiswill typically include identifying events such as a number of times thesubject has changed posture, how many steps have been walked, how manytimes appliances have been used or the like. Identification of events istypically performed by analysing one or combined sensor readings todetect the gradation of the cluster of activities for the particularpatterns of readings, with the number of resulting events then beingtallied to generate the score. The domain rule will therefore alsospecify which criteria should be used to identify subject events, andthen how these should be tallied or otherwise interpreted in order togenerate a domain score.

In any event, at step 560 the sensor data and domain rule are used todetermine a domain score, and specific examples of this process will bedescribed in more detail below.

At step 570, it is determined if all domains are complete, and if notthe process returns to step 530 to select a next domain. Otherwise, atstep 580, the domain scores are used to determine an activity level,also known as an activities of daily living level, which could be in theform of a single combined score, a pattern or relative scores, or thelike, depending on the preferred implementation.

The process for ongoing monitoring of a subject's activity levels willnow be described with reference to FIGS. 6A and 6B.

In this example, at step 600 a reference activity level is determined byhaving the server 250 determine domain scores during a reference timeperiod. Thus, it will be appreciated that this involves performing theprocess of FIG. 5 during a reference time period. The reference timeperiod can be of any appropriate duration, but typically extends over atleast one week, and more typically a month, with individual daily scoresbeing generated allowing variations in activity levels of the subject tobe determined, and in particular allowing an assessment to be made ofany patterns, such as any correlation of activity levels with particulardays of the week.

During this process, a physical activity capability assessment can beperformed at step 605. This can be performed in any suitable manner,such as through third party assessment by a clinician or other carer, orthrough self-assessment by the subject or a combination of theseapproaches. This allows a correlation between a daily activity level andfunctional capabilities to be established at step 610, for example todetermine if a particular domain score corresponds to a particularcondition, circumstance, episode, or the like. As part of this initialmonitoring process, the subject may be assessed to decide in whatcircumstances actions might need to be performed, allowing an actionrule to be created defining when and what actions are required, as willbe described in more detail below.

At step 615, the server 250 monitors a current activity level, forexample by determining domain scores in a current time period using theprocess outlined above with respect to FIG. 5.

The current activity level and, in particular the subject domain scores,are then compared to respective thresholds at step 620. The thresholdsare typically in the form of a threshold range, corresponding to anexpected range of domain scores, as determined using the referencedomain scores. The ranges could be based solely on the absolute domainscores, with a fixed range either side of the score. More typicallyhowever, the ranges are based on variations in the domain score duringthe reference time period, so if a high degree of variation is typicalfor a subject, then a correspondingly large range would be considered asnormal. The thresholds could also be based on the particular time periodbeing measured, for example to compare to a corresponding reference timeperiod, so for example, if the current day is a weekday, then thecurrent activity levels could be compared to thresholds established fromreference activity levels also measured on weekdays. However, morespecific time periods could be used, for example to take into accountroutine activities. For example, if the individual fails to attend aregular appointment, this could be indicative of an issue, which in turncould be used to trigger an alarm or the like. Additionally and/oralternatively, the thresholds could be based on conditions suffered bythe subject, for example to define a narrower threshold range for adomain that is of particular importance to the subject, and theirability to perform day to day activities.

At step 625 it is determined if the thresholds are exceeded, the processproceeds to step 630 to determine if any action needs to be taken. Inthis regard, the need to take action will typically differ for eachsubject, and may therefore be uniquely defined depending on therequirements of the subject. Accordingly, the server 250 can access anaction rule for the subject and determine from this what action isrequired, if any.

In this regard, whether action is required could depend on a range ofdifferent factors, such as the particular threshold(s) that areexceeded, the degree by which these are exceeded, whether this is thefirst time the threshold is exceeded, a time since this or otherthresholds were last exceeded or the like. For example, the first time athreshold is exceeded may not warrant further action, but if the samethreshold is exceeded two days running, or three times within a week,then action might be required.

The nature of the actions that can be performed will vary depending onthe preferred implementation, but typically includes alerting one ormore users of the system, including but not limited to care givers,relatives, assigned medical personnel, emergency services, or the like.It will also be appreciated that the user could be selected based on theresults of the comparison, as defined by the action rule. So forexample, the first time a threshold is exceeded, a relative or carermight be informed, whilst the second time, medical services could beinformed.

Thus, it will be appreciated that action rules can be establisheddetermining what actions are required depending on the results of thecomparison, and also on other factors, such as the results of previouscomparisons, and/or actions taken. Having established what action isrequired, this is then typically performed at step 635. For example, ifone or more users are to be alerted, the server 250 generates an alertnotification, such as a text message, email, or the like, and thentransfers this to a relevant client device 230 via the communicationsnetwork 240 at step 640.

At step 645, the server 250 stores an indication of the activity level,and optionally additional information, such as the subject domainsscores and details of any actions performed.

At step 650, the server 250 generates an activity level indication,typically by generating a graphical representation of the activity leveland/or results of the comparison, with this being displayed to users ondemand, for example as part of a dashboard displayed on a webpage,application, or the like at step 655. This allows users to access andview activity levels at any time, allowing ongoing monitoring of currentand historical activity levels to be performed, which in turn can formpart of an ongoing functional capability assessment program.

It will be appreciated that the above described process would typicallybe performed continuously on an ongoing process. As part of thisprocedure, at step 660, reference activity levels may be optionallyupdated taking into account current activity levels, for example, toreflect ongoing progression of a condition and hence user's activitycapabilities. Thus, if a subject is undergoing rehabilitation and theiractivity capabilities gradually improve, then it might be desirable toupdate the reference activity levels to reflect these changes.Alternatively if an individual undergoes a slight but significantdecline in the behavioural patterns that is related to psychologicaldecline e.g. mild cognitive impairment, then the comparison will be madeagainst new update reference ADL profile or score to either put a newintervention to manage or improve the impairment.

A specific example process will now be described in more detail.

In this example, the activity monitoring system aggregates informationfrom wireless sensors placed in a person's living environment togather/infer ambient, physical, physiological, and psychologicalconditions of that person to establish a profile of the subject'sfunctional and health status to enable support from informal or formalcarers. The functional status uses the activities daily living frameworkused in clinical settings.

An example configuration is shown in FIG. 7, in which sensors areprovided in the dwelling D, thereby detecting home activity, with sensordata being collated by a hub in the form of a sensor platform 720, withsensor data being passed to a cloud based monitor 750, which collects,processes, and presents the results to users, such as family, carers andresidents, via respective client devices 730.

The main processes involved can be categorized as:

-   -   Data Collection: where continuous raw data from non-intrusive        sensors in a home environment, and physiological data such as        blood pressure, body temperature from wireless clinical        measurement devices are gathered wirelessly by a server;    -   Data Analysis: data collected are transmitted to a cloud to        extract defined daily activities, namely meal (preparing and        attending to a meal), transfer (postural changes e.g. waking        from bed), dressing/grooming (ability to clothe and any        appearance related task), hygiene (attending to washing, shower,        etc.), and mobility (ability move around within home and/or        outside), from raw sensor data through human behavior pattern        analyses algorithms; evaluating extracted ADL patterns through        ADL scores to understand daily health status and functional        status of an independent living, at home (such as an older        person);    -   Presentation: The data is then presented in numerical and        graphical presentation relevant to the end-user's        interpretation, including but not limited to:        -   Self-monitoring/management (for resident at home being            supported), family members, friends or neighbours engaged in            the support for the wellbeing of the person being supported            (e.g. An elderly parent). This would be provided via a            lifestyle tablet PC or other similar client device.        -   Family members or carers to gain access to wellbeing            parameters that provide an insight into the lives of their            elderly parent living alone through the family portal by a            web browser.        -   Healthcare practitioner or clinical services to correspond            any significant health changes to in correspondence to daily            functional status.

The key domains of activities of daily living (ADL) comprise mobility,transfer, hygiene, dressing, and meal preparation. While such domainsare assessed typically in a clinical setting by a clinical personnel, isthis approach is not only limited by the subjectivity of the ADLassessment but also by the unreflective, artificial environment of andtasks on which the subject is assessed. However, by using the abovedescribed approach, sensor data can be used to generate quantifiedindicators, removing the subjectivity, and ensuring a more reliable andconsistent measure is established.

An example of the process for extracting an ADL score is shown in FIG.8.

In this example, raw sensor data is obtained at 800, from sensors placedat somewhat ‘invisible’ and non-intrusive position to residents in theirhome environment are communicated wirelessly via low-powered protocol(e.g. via ZigBee or Bluetooth or WiFi) to a local sensor hub. Dependingon the ADL domain of interest, activities are gathered from either oneor multiple sensors, whether specific to a location or multipleinteractions of home appliances and fittings. For example, to identify ameal preparation in modern homes, a combination of power outlet sensorsto detect kitchen electrical appliances and/or stove, contact sensor candetect use of refrigerator and pantry cupboards to access food items,etc. would determine a meal activity.

Accordingly, it is determined if there are multiple sensors at 810. Ifnot, the sensor data is processed at 820 to generate scores for eachdomain ADL(x_(i)) . . . ADL (x_(m)) at 840 . . . 850. If there aremultiple sensors, the sensor data is fused at 830 before scores aregenerated each domain ADL(y_(i)) . . . ADL (y_(n)) at 860 . . . 870.This is then used to compute an ADL score at 880, using equation (1):

ADL₀=Σ_(i=1) ^(m)α_(i)·ADL(x _(i))+Σ_(j=1) ^(n)β_(j)·ADL(y _(j))   (1)

Examples of the types of raw sensors relating to the placementcorresponding to home activity are described below in Table 1.

TABLE 1 Sensor Type Data Gathered Place of installation Motion sensorIncidents of motion within Ceiling in all rooms operating range installPower sensor Current draw of various Wall power outlets appliances anddevices Temperature/ Temperature and humidity Bathroom and kitchenHumidity sensor readings with a frequency in the house dependent onsensor and/or application Accelerometer Movements in the bed/chairAttached to the bottom of the bed/chair Circuit meter Stove usagemonitoring Attached to the electrical switchboard panel Reed switchDoors open/close Bedroom wardrobe, kitchen pantry door and freezer door

From a somewhat comprehensive deployment of simple environmentalwireless sensors instrumented in a 2 bedroom home, ADL domain score canbe determined from corresponding sensor(s) (as shown in Table 2) tocapture the relevant associated home activity(s). In particular, in thisexample, score for five activity domains are captured relating tomobility, hygiene, dressing, postural transfer (lying to standing), andpreparing meals. For each activity, a score is computed that will beused later to calculate ADL correlations with health and wellbeingstatus to output a holistic ADL score.

TABLE 2 Temper- Cir- ature/ Acceler- cuit Reed Activity Motion PowerHumidity ometer meter switch Mobility ✓ Hygiene ✓ ✓ Dressing/ ✓ ✓Grooming Transfer ✓ ✓ Meal ✓ ✓ ✓ ✓

Examples of the calculation of domain scores will now be described inmore detail for each domain, with reference to the domain scores shownin FIGS. 9A to 9F and sensor readings shown in FIGS. 10A to 10E.

Mobility

Daily indoor steps are computer to reflect the mobility status of theresident. This involves only motion sensors deployed at every room ofthe house. FIG. 10A shows an example of motion sensor firings fromdifferent rooms, represented in different colours of spikes over a day.

This information is then used to compute steps between rooms fromconsecutive firings between two motion sensors in different rooms asshown in Table 3.

TABLE 3 Dining Bed- Bath- Steps room Lounge Kitchen Laundry room roomDining 0 5 10 9 13 12 room Lounge 5 0 8 4 8 7 Kitchen 10 8 0 10 16 15Laundry 9 4 10 0 6 4 Bedroom 13 8 16 6 0 5 Bathroom 12 7 15 4 5 0

Together with motion sensor firings, this is used to conclude the indoorsteps as a score to represent indoor mobility status, as shown inEquation (2).

Mobility=Σ_(ij)Steps(Room_(i), Room_(j))*(Motion_(i)−Motion_(j))   (2)

Although this approach is not as accurate as that of a step countwearable pedometer, this assessment is based on a relative change of anindividual's indoor mobility status rather than accurate, absolute dailysteps. Therefore, mobility calculated from motion sensors is sufficientto reflect to reflect change based on their estimated indoor activities.

An example of a derived mobility score is shown in FIG. 9A.

Hygiene

This activity represents how well the subject maintains hygiene status,inferred through bathroom usages. Hygiene scores thus can be calculatedthrough changes of humidity readings from the temperature/humiditysensor deployed in the bathroom, together with the bathroom motionsensor. From humidity changes, it is easy to determine bathroom usagessuch as taking showers, as illustrated in FIG. 10B, around 19:00.

To correctly compute hygiene scores, it is necessary to consider bothbathroom motion sensor readings and humidity changes as shown inEquation (3).

Hygiene=Σ_(t=0) ^(23:59 :59)|Humidity_(t+1)−Humidity_(t)|*Motion_(t)  (3)

An example of daily hygiene score corresponding to washroom/bathroominferred showering activity is shown in FIG. 9B. This could also befurther confirmed by additional sensors, such as flow sensors, vibrationsensors or the like, to differentiate different taps being used.

Dressing

A contact sensor, such as a reed switch is attached to the door of abedroom wardrobe, and thus can infer dress activity through statechanges of the reed switch and bedroom motion sensor in Equation (4).

Dress=Σ_(t+0) ^(23:59:59)Reed_(t)*Motion_(t)   (4)

An example of daily dressing activity is shown in FIG. 9C.

Transfer

Postural transfers related to lying to standing and vice-versa aremeasured from integrated information collected from bedroom motionsensor and accelerometers. The bedroom motion sensor can be used toindicate when bedroom is occupied during a day. The 3-axis accelerometerattached to the bed mattress, detects bed vibrations during sleep andget up from/lie down on bed. FIG. 10C shows original 1Hz vibration datafrom accelerometer of one night's sleep.

Considering the bed movement at every time point as a three-dimensionalvector, {right arrow over (v)}(x, y, z), it is possible to compute theangles between vectors and thus detect bed postural transferautomatically by counting the occurrence of torso inclination angles tolower limb. For instance, in FIG. 10D, there are 4 postural transferdetected if a threshold of inclination angle is set to 52°.

Thus, the transfer score can be computed using equation (5):

Transfer=ΣAngle≥threshold   (5)

It would be noted that the value of inclination angle threshold willdiffer among different individuals, and would therefore typically needto be assessed during the baseline time period.

An example of a resulting daily transfer activity score corresponding toposture transfer tasks from state of lying in bed to getting up is shownin FIG. 9D.

Section 3.5 Meal

Extracting meal activity can be difficult as preparing a meal typicallyinvolves multiple actions. Data from multiple sensors placed in thekitchen thus need to be gathered collectively to infer a meal activity.FIG. 10E illustrates three days sensor firings for all meal preparationrelated sensors in a home.

In order to more accurately identify meal preparation, clusteringtechniques are used to extract the meal preparing activities fromrelated sensor data, based on an assumption that meal preparation willnormally involve related sensor firings in a short time period. Byassigning various probabilities of sensors related to meal activity, itis possible to compute blocks of time periods of meal preparation withhigh probability, as illustrated by boxes in FIG. 10D.

Using this configuration, a meal score can be derived using equation(6):

Meal=Σ_(i)=i^(number of meals)Σ_(jεRelated Sensors) Pr(j)   (6)

An example of daily meal preparation activity scores corresponding tocluster of kitchen activities that constitute to major meal activitiesare shown in FIG. 9E.

Objective Assessment of ADL Scores

In a typical assessment conducted in a clinical setting, ADL is scoredin binary (0/1) from which the aggregated score equates to a functionalindependence score. As the functional assessment is conducted in anartificial setting, it is unreflective on the type or frequency orduration of home activities that a subject is likely to perform dailyand hence contribute to the ADL domains that correspond to the healthand wellbeing. Hence, the current system derives an ADL score to reflectone's daily home activities that can then be compared against baseline,typically measured for a healthy state of the individual's wellbeing.

To use objective ADL scores to reflect health and wellbeing status of anindividual, it is necessary to demonstrate that activity measurementsacquired through environmental sensors can be effectively correlatedwith physiological (if related to illness that relates to a decline inmeasurement e.g. blood pressure or body temperature) or physical(gathered by resident's perception) decline.

To achieve this, data were collected and then a comparison performedbetween the objective ADL measure and standard subjective techniques.

To achieve better regression results, raw ADL values are normalized bycomputing Z-scores:

$\begin{matrix}{{ADL} = \frac{{ADL} - {{Mean}({ADL})}}{{Std}({ADL})}} & (7)\end{matrix}$

To find correlations between ADLs and daily wellbeing, weeklyquestionnaires and self-report dairies were provided to a number ofsubjects to understand their daily wellbeing. FIG. 11A shows continuous80 days wellbeing scores of a resident from her self-reports and medicalrecords, with 0 representing a very bad day and 100 representing a verygood day. It will be noted that in this instance wellbeing starteddropping on Day 75 reaching 0 in Day 80. This was later noted to be dueto the resident being subjected to a sudden event of neurologicaldecline in Day 75, and eventually resulted in the resident hospitalisedon day 81.

Rescaled ADLs measured through this time period are shown in FIG. 11B.During the last week, due to neurological decline event, the subject'shome activities were confined to the bedroom where the subject was foundto be inactive. That is why the bedroom related activities, i.e. Dressand Transfer, have higher values. In the same time, other activities,i.e. Mobility, Hygiene, Meal, dropped significantly.

Pairwise correlation to minimize harmful effects of multicolinearity areshown in Table 4.

TABLE 4 Mobility Hygiene Dress Transfer Meal Mobility 1 .04 −.23 .38 .6Hygiene 1 .03 .05 −.09 Dress 1 .35 −.22 Transfer 1 .11 Meal 1

Because no two activities show strong linear correlations, allactivities of this subject were used to compute ADL scores. Specificallymultivariate linear regression model was used to derive ADL scores fromdaily activities, as shown in equation (8) and (9):

ADL₀=α₀+Σ_(i=1) ^(n)α_(i)*Activity_(i)   (8)

ADL₀=α₀+α₁·Mobility+α₂·Hygiene+α₃·Dress+α₄·Transfer+α₅·Meal   (9)

Table 5 lists coefficients computed to reflect on the weighted influenceof specific ADL domains towards the overall ADL score from dailyactivity data of the subject.

TABLE 5 a₀ a₁ a₂ a₃ a₄ a₅ Coefficients 94** 7.3** 3.6** −11.2** −5.3**1.8

The weighted ADL score for this subject, according to their ADL domains,is therefore:

ADL₀=94+7.3−Mobility+3.6−Hygiene−11.2·Dress·5.4·Transfer+1.8·Meal   (10)

In this subject, increase in ADL domains Dressing and Transfer reflectnegatively on their health and wellbeing status. Dressing, however, maybe skewed by the wardrobe being left open and the contact sensor notreflecting accurately of accessing clothes only when there is anactivity of dressing. It will also be appreciated however that this canbe obviated by knowing if the individual leaves the wardrobe open ashabit, and hence taken into account by measurements collected during thereference time period.

The ADL score over the 9 weeks leading to the subject's neurologicalevent is shown clearly as fluctuations of ADL score over the last 3weeks in FIG. 11B.

Representations

To promote self-monitoring and easily understand of results, graphicalrepresentation of resulting ADL scores can be used, and examples ofthese are shown in FIGS. 12A to 12C.

In the dashboard representation 1200 shown in FIG. 12A, coloured rays1201 are used to represent the domain score associated with each of thefive domains 1202, with the length of each ray representing a gradingscore of health and wellbeing parameters relevant to functionalindependence including the mobility domain and overall score of ADL,with each grading score being compared against a respective baseline orreference score.

Selecting an “activity” option presented in the menu 1203 allows theuser to view details of daily activities, as shown in FIG. 12B. In thisexample, the dashboard representation 1200 includes a graph 1210 of ADLscores calculated using the linear regression formula discussed above,for a specific activity shown on tab 1211. The scores can be colourcoded, for example using a traffic light scheme representing a good,below average and bad functional capability, with good days being shownfor Mon, Wed, Thu, Sat and Sun, and below average days shown for Tue andFri, in this example.

Moreover, by tapping the magnifying glass 1212, uses can zoom in to seefurther details of scores of normalized activities, as shown in FIG.12C. This representation gives an easy-to-understand visual impressionsof how ADL score is computed, showing the score for each segmented intosubject ADL domains. For instance, if we take Monday as a normal goodday of functional capability, then this day can be used as a signaturepattern particularly related to this individual. For Tuesday and Friday,the big variation of their patterns from the signature pattern willexplain why domains have led ADL scores in amber.

Accordingly, the above described system and method provides an objectiveassessment/measure of ADL specific to a subject's own living profile,including the environment in which they live. In one example, anactivity level indicator (also referred to as an ADL score) isdetermined based on sensor readings obtained from sensors placed withinthe subject's living environment and which are consequentlynon-intrusive. Furthermore, the resulting ADL score can be compared to abaseline score calculated within the individual's own livingenvironment, which therefore allows this to represent changes in theindividual's activity capabilities, as reflected by their everydayliving routines.

Thus, this provides an objective assessment of ADL in a settingrepresentative of the person's living environment. By using sensor datacollected from sensors in the subject's environment, this ensures theassessment is objective and reflective of the subject's normal course ofdaily activities and interactions in their own living setting. Thiswould be more reflective of a functional capacity pertaining to thesubject's own self-profile, and avoids the need for subjectiveassessment, either by the individual themselves, or by medicalpractitioners, or other specialised personnel.

Furthermore, this not only improves the determination of change/progressthrough time the ADL from a subject's own benchmark (baseline) but alsoprovide a more regular assessment for their family and carer to haveincreased and timely access to act on any early decline in function.Thus, a more timely assistance or intervention can be undertaken.

Compared with existing methods, this provides a more consistent approachand is reflective of human visual assessment and of their livingenvironment, respectively. This approach is also less intrusivepsychologically (once they become comfortable with the sensors in theirenvironment) that it is less instructive and aligned to their lifestyleactivities.

Thus, in comparison with previous techniques, the current techniquedetermines an activity indicator measured in an objective and continuousmanner, which as a result is more likely to be more consistent andaccurate. Unlike the assessment performed simulated in a clinicalsetting, the activity indicator is based on the individual's own livingenvironment and also referenced to one's functional state, andtherefore, changes in the activity indicator would be more reflectiveand representative to the subject's activity profile and health status.

Unlike methods that involve using wearable or visual sensor systems toderive measures of ADL, the ADL in this invention derived fromnon-intrusive sensors placed around the subject's living environmenttherefore placing less burden of added daily task and/or interactionwith technology to achieve. Furthermore, this allows detection based onvarious activity patterns reflective of day-to-day health status,forming a much more integrated with their physiological and somewhatpsychological condition.

Automatic determination of ADLs enables objective assessment offunctional independence, particularly in a home environment for theelderly people living independently. These ADL scores can be used tosupport older people living alone in self-management of their functionalindependence; and simultaneously provide the capacity for family membersto provide better support to their elderly parents living aloneremotely. Furthermore, an automated ADL assessment feature could alsoprovide health care providers the capacity to monitor older peoples'health care status more regularly, and provide a more timely and earlyintervention through telehealth. The techniques could be applied tonumerous different scenarios, including but not limited to:

-   -   Assessment of older people in their functional independent        assessment and wellbeing;    -   Early Intervention to provide assistive technologies in people        losing their functional ability to live independently;    -   Early intervention of care assistance to support the limited        functional capability or disability;    -   As in above, people with disability could be supported        similarly;    -   A parent needing to watch their teenage kids in the home should        they occasionally need to go out to attend to activities like        shopping;    -   Efficiency of work environment e.g. Nursing service attending to        institutional care setting to determine resource allocation;    -   Behavioural mapping for subject assessment and needs;    -   Behavioural mapping that represent any psychological decline;    -   Resident care monitoring and focused care to the needed        resident;    -   Contribute to other ADL index measurements such as Barthel        index; and,    -   Contribute to assessment instruments e.g. ACAT, determining        eligibility or prevention to residential care facility.

The term “subject” will be understood to include a patient or otherindividual that is being monitored.

Throughout this specification and claims which follow, unless thecontext requires otherwise, the word “comprise”, and variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated integer or group of integers or steps but not the exclusionof any other integer or group of integers.

Persons skilled in the art will appreciate that numerous variations andmodifications will become apparent. All such variations andmodifications which become apparent to persons skilled in the art,should be considered to fall within the spirit and scope that theinvention broadly appearing before described.

1. A method of monitoring activity capabilities of a subject, the method including, in at least one processing device: a) determining sensor data indicative of sensor readings for each of a plurality of sensors, the sensors being positioned within a living environment of the subject and the sensor data for each sensor being at least partially indicative of one or more activities performed by the subject; b) for each of a plurality of activity domains, determining a domain score indicative of a level of activity within the respective activity domain, the domain score being determined using sensor data from a respective combination of sensors associated with the respective domain; c) determining a reference activity level using reference domain scores measured during a reference time period; d) determining a current activity level using current domain scores measured during a monitoring time period; and, e) generating an activity indicator at least partially in accordance with the current activity level and the reference activity level, the activity indicator being at least partially indicative of differences between the current activity level and the reference activity level, thereby providing feedback on the activity capabilities of the subject.
 2. A method according to claim 1, wherein the method includes: a) determining an activity level score by combining the domain scores; b) determining the reference activity level using the activity level score measured during the reference time period; and, c) determining the current activity level using the activity level score measured during the monitoring time period.
 3. A method according to claim 2, wherein the combination includes at least one of: a) a sum; and, b) a weighted sum.
 4. A method according to claim 1, wherein the method includes: a) determining an activity pattern indicative of relative values of domain scores; b) determining the reference activity level using the activity pattern measured during the reference time period; and, c) determining a current activity level using a current activity pattern measured during the monitoring time period.
 5. A method according to claim 1, to wherein the method includes: a) comparing the current activity level to the reference activity level; and, b) generating the activity indicator at least partially in accordance with results of the comparison.
 6. A method according to claim 5, wherein the method includes comparing at least one of: a) each current domain score to an equivalent reference domain score; b) each current domain score to a respective reference range derived from an equivalent reference domain score; c) a current activity level score to a reference activity level score; d) a current activity level score to a respective reference range derived from a reference activity level score; e) a current activity pattern to a reference activity pattern; and, f) a current activity level to a reference activity level measured during a corresponding time period.
 7. A method according to claim 6, wherein the method includes: a) determining a condition suffered by the subject; and, b) determining, at least partially in accordance with the condition, at least one of: i) an activity level score; ii) a domain score; iii) a reference domain score range; and, iv) a reference activity level range.
 8. A method according to claim 5, wherein the method includes: a) determining an action rule; and, b) selectively performing an action in accordance with the action rule and in response to the results of the comparison.
 9. A method according to claim 8, wherein the action includes: a) generating an alert notification; and, b) providing the alert notification to a user by transferring the alert notification to a client device of the user via a communications network.
 10. A method according to claims 1, wherein the method includes generating a representation indicative of at least one of: a) results of a comparison; b) current domain scores; c) reference domain scores; d) current activity level scores; e) reference activity level scores; f) current activity level patterns; g) reference activity level patterns; and, h) the activity indicator.
 11. A method according to claim 10, wherein the method includes providing the representation to a client device via a communications network.
 12. A method according to claim 1, wherein the method includes determining a domain score using sensor data from a respective combination of sensors.
 13. A method according to claim 12, wherein the respective combination of sensors for each domain is determined based on at least one: a) a sensor type; and, b) a sensor location.
 14. A method according to claim 1, wherein the sensors include at least one of: a) motion sensors; b) power sensors that monitor operation of appliances; c) temperature sensors; d) humidity sensors; e) accelerometers; and, f) door sensors.
 15. A method according to claims 1, wherein the activity domain includes: a) hygiene; b) nutrition; c) mobility; d) transfer; and, e) dressing/grooming.
 16. A method according to claim 1, wherein the method includes, for at least one domain: a) identifying events using sensor data from the sensors; and, b) determining the domain score using at least one of: i) a sum of a number of events during a time period; and, ii) a sum of a number of clusters of events during a time period.
 17. A method according to claim 16, wherein the method includes identifying events by comparing sensor data to a number of signatures, each signature being indicative of a respective event.
 18. Apparatus for monitoring activity capabilities of a subject, the apparatus including: a) a plurality of sensors, the sensors being positioned within a living environment of the subject; and, b) at least one processing device that: i) determines sensor data indicative of sensor readings for each of a plurality of sensors, the sensors being positioned within a living environment of the individual and the sensor data for each sensor being at least partially indicative of one or more activities performed by the individual; ii) for each of a plurality of activity domains, determines a domain score indicative of a level of activity within the respective activity domain, the domain score being determined using sensor data from a respective combination of sensors associated with the respective domain; iii) determines a reference activity level using reference domain scores measured during a reference time period; iv) determines a current activity level using current domain scores measured during a monitoring time period; and, v) generates an activity indicator at least partially in accordance with the current activity level and the reference activity level, the activity indicator being at least partially indicative of differences between the current activity level and the reference activity level, thereby providing feedback on the activity capabilities of the subject.
 19. Apparatus according to claim 18, wherein the apparatus includes a hub provided in the living environment, the hub being adapted to communicate with each of the sensors and provided the sensor data to the at least one processing device, via a communications network.
 20. Apparatus according to claim 19, wherein the apparatus includes a processing system including at least one processing device, the processing system communicating with one or more client devices via communications network, to at least one of: a) provide alert notifications to the client devices; and, b) allow the client devices to display a representation indicative of at least one of: i) results of a comparison; ii) current domain scores; iii) reference domain scores; iv) current activity level scores; v)reference activity level scores; vi) current activity level patterns; vii) reference activity level patterns; and, viii) the activity indicator. 